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Abstract #3778

Deep Learning Based 3D Whole Liver and Spleen Segmentation for Quantitatively Reproducible Liver Fat and Iron Deposition Grading

Ahmed Gouda1, Saqib Basar1, Yosef Chodakiewitz2, Rajpaul Attariwala1, Sean London2, and Sam Hashemi1
1Voxelwise Imaging Technology Inc., Vancouver, BC, Canada, 2Prenuvo, Vancouver, BC, Canada

Synopsis

Keywords: Liver, Fat, Hepatic Steatosis Quantification, Iron Deposition Detection, Dixon, FSF, Dual-echo MRIOver the years, Magnetic Resonance Imaging (MRI) has become the optimal noninvasive method to quantify liver steatosis and to detect hepatic iron deposition. The conventional manual sampling technique of liver fat quantification at multiple regions is complex, inefficient and a time-consuming process. In addition, it may produce varying results for the heterogeneous fat deposition, which is prone to radiologist’s subjectivity. In this research, we propose a fully automated artificial intelligence (AI) based method for hepatic steatosis quantification and hepatic iron deposition detection using whole liver and spleen volume segmentation.

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